---
title: "nCov 19 - As on 27/3/2020"
output:
flexdashboard::flex_dashboard:
orientation: rows
vertical_layout: fill
social: [ "linkedin", "facebook", "menu"]
source_code: embed
---
```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(knitr)
library(DT)
library(rpivotTable)
library(ggplot2)
library(plotly)
library(dplyr)
library(openintro)
library(highcharter)
library(coronavirus)
library(lubridate)
library(janitor)
```
```{r}
data <-read_csv("covid_19_data.csv") %>%
filter(ObservationDate=="03/27/2020") %>%
clean_names()
data_raw<-read_csv("covid_19_data.csv") %>%
clean_names()
```
Summary
===========================================
Row
------------------------------------------
### Alert Level
```{r}
valueBox(paste("4"),
color = "red",
icon = "fa-calendar")
```
### Total Cases
```{r}
valueBox(sum(data$confirmed),
icon="fa-user")
```
### Total Deaths
```{r}
gauge((sum(data$deaths)),
min = 0,
max = 100000,
gaugeSectors(success = c(0, 25000),
warning = c(25001, 50000),
danger = c(50001, 100000),
colors = c("yellow", "orange", "red")))
```
### Total Cases in NZ
```{r}
valueBox(data %>%
filter(country_region=="New Zealand") %>%
select(confirmed) %>%
colSums(),
icon="fa-user")
```
Row
------------------------------------
```{r}
data(worldgeojson, package = "highcharter")
data<-data %>%
clean_names() %>%
filter(!country_region %in% 'Others') %>%
group_by(country_region) %>%
summarise(total_confirmed = sum(confirmed))
highchart() %>%
hc_add_series_map(worldgeojson, data, value = 'total_confirmed', joinBy = c('name','country_region')) %>%
#hc_colors(c("darkorange", "darkgray")) %>%
hc_colorAxis(stops = color_stops()) %>%
hc_title(text = "Countries with nCov exposure")
```
Trends
===============================================
Row
---------------------------
```{r}
p1 <- data_raw %>%
mutate(observation_date=as.Date(observation_date, format="%m/%d/%Y")) %>%
group_by(observation_date) %>%
summarise(confirmed = sum(confirmed) , deaths = sum(deaths), recovered = sum(recovered)) %>%
ggplot(aes(observation_date, (confirmed))) +
geom_line() +
theme_minimal() +
ggtitle("Worldwide Cases")
ggplotly(p1)
```
```{r}
p1 <- data_raw %>%
filter(country_region=="New Zealand") %>%
mutate(observation_date=as.Date(observation_date, format="%m/%d/%Y")) %>%
group_by(observation_date) %>%
summarise(confirmed = sum(confirmed) , deaths = sum(deaths), recovered = sum(recovered)) %>%
ggplot(aes(observation_date, (confirmed))) +
geom_line() +
theme_minimal() +
ggtitle("NZ Cases")
ggplotly(p1)
```